Usporedite metode

	Objašnjivi Stacking Ansambl	Bagging Ensemble	Povećanje gradijenta	Slučajna šuma	XGBoost
Područje	Strojno učenje	Ansambl učenje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Godina nastanka	1992 (stacking); 2010s–2020s (explainable extensions)	1996	2001	2001	2016
Tvorac	Wolpert, D. H. (stacking); XAI integration developed across the community	Leo Breiman	Friedman, J. H.	Breiman, L.	Chen, T. & Guestrin, C.
Vrsta	Ensemble meta-learning with post-hoc or intrinsic interpretability	parallel ensemble	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Temeljni izvor	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Drugi nazivi	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	bootstrap aggregating	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Srodne	4	4	5	4	5
Sažetak	Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.